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Selected Semantic Web Trends, Progress, and Directions . Deborah McGuinness Acting Director and Senior Research Scientist Knowledge Systems, AI Laboratory Stanford University http://www.ksl.stanford.edu/people/dlm CEO McGuinness Associates

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Selected semantic web trends progress and directions l.jpg

Selected Semantic Web Trends, Progress, and Directions

Deborah McGuinness

Acting Director and Senior Research Scientist

Knowledge Systems, AI Laboratory

Stanford University

http://www.ksl.stanford.edu/people/dlm

CEO McGuinness Associates

(soon Tetherless World Constellation Chair RPI)


Semantic web layers and dlm l.jpg
Semantic Web Layers(and DLM)

Ontology Level

  • Language (OWL, IKL, DAML+OIL, OIL, CLASSIC, …)

  • Environments (FindUR, Chimaera, Ontolingua, OntoBuilder / Server, Sandpiper Tools, Cerebra, …)

  • Services OWL-S, SWSL, …

  • Standards body leverage (W3C’s WebOnt, W3C’s Semantic Web Best Practices, EU/US Joint Com, OMG ODM, W3C’s RIF, Scientific Markup Standards, NAPLPS…)

    Query

  • OWL-QL, …

    Rules

  • RIF, SWRL , …

    Logic

  • Description Logics, FOL

    Proof

  • PML, Inference Web Services and Infrastructure

    Trust

  • IWTrust

http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/

Deborah L. McGuinness


Applications l.jpg
Applications

Applications

  • Virtual Solar Terrestrial Observatory, SESDI, SKIF, SSOA, BISTI, …

  • Cognitive Assistants (PAL, CALO, GILA, …)

  • Domain ontologies (immunology,

    atmosphere, volcano, …) & environments

  • Tools academic & industry (Sandpiper,…)

  • Startups: Health Information Flow,

    Blue Chip Expert, Katalytik, Radar Networks…

Deborah L. McGuinness


Two trends l.jpg
Two Trends

  • Trust/Explanation

    • Trust Motivation and Requirements

    • Inference Web approach

      • Interlingua: Proof Markup Language

      • Explanation & Provenance Browsing / Abstraction / Presentation Strategies (examples from DARPA PAL, NSF TAMI, DTO NIMD, NSF VSTO)

      • Trust (examples from Wikipedia study for explainable knowledge aggregation with extensions to text analytics, connection to NSF TAMI)

  • Semantic Integration of Scientific Data

    • Virtual Observatories (e.g., NSF-funded Virtual Solar Terrestrial Observatory)

    • Semantically-Enabled Scientific Data Integration (NASA-funded)

  • Conclusion / Discussion

Deborah L. McGuinness


Virtual observatories l.jpg
Virtual Observatories

Scientists should be able to access a global, distributed knowledge base of scientific data that:

  • appears to be integrated

  • appears to be locally available

    But… data is obtained by multiple instruments, using various protocols, in differing vocabularies, using (sometimes unstated) assumptions, with inconsistent (or non-existent) meta-data. It may be inconsistent, incomplete, evolving, and distributed

Deborah L. McGuinness


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Virtual Observatory Defined

  • Workshop: A Virtual Observatory (VO) is a suite of software applications on a set of computers that allows users to uniformly find, access, and use resources (data, software, document, and image products and services using these) from a collection of distributed product repositories and service providers. A VO is a service that unites services and/or multiple repositories.

  • VxOs - x is one discipline

Deborah L. McGuinness


Virtual observatories in practice l.jpg
Virtual Observatories in Practice

Make data and tools quickly and easily accessible to a wide audience.

Operationally, virtual observatories need to find the right balance of data/model holdings, portals and client software that a researchers can use without effort or interference as if all the materials were available on his/her local computer using the user’s preferred language.

They are likely to provide controlled vocabularies that may be used for interoperation in appropriate domains along with database interfaces for access and storage and “smart” search functions and tools for evolution and maintenance.

Deborah L. McGuinness


Virtual solar terrestrial observatory vsto l.jpg
Virtual Solar Terrestrial Observatory (VSTO)

  • a distributed, scalable education and research environment for searching, integrating, and analyzing observational, experimental, and model databases.

  • subject matter covers the fields of solar, solar-terrestrial and space physics

  • it provides virtual access to specific data, model, tool and material archives containing items from a variety of space- and ground-based instruments and experiments, as well as individual and community modeling and software efforts bridging research and educational use

  • 3 year NSF-funded project just beginning the second year

Deborah L. McGuinness


Content coupling energetics and dynamics of atmospheric regions web l.jpg
Content: Coupling Energetics and Dynamics of Atmospheric Regions WEB

Community data archive for observations and models of Earth's upper atmosphere and geophysical indices and parameters needed to interpret them.

Includes

browsing capabilities by periods, instruments, models, …

Deborah L. McGuinness


Content mauna loa solar observatory l.jpg
Content: Mauna Loa Solar Observatory Regions WEB

Near real-time data from Hawaii from a variety of solar instruments.

Source for space weather, solar variability, and basic solar physics

Other content used too – CISM – Center for Integrated Space Weather Modeling

Deborah L. McGuinness


Content volcanoes mt spurr ak 8 18 1992 eruption usgs l.jpg
Content: Volcanoes… Regions WEBMt. Spurr, AK. 8/18/1992 eruption, USGS

http://www.avo.alaska.edu/image.php?id=319

Deborah L. McGuinness


Eruption cloud movement from mt spurr ak 1992 l.jpg
Eruption cloud movement from Mt.Spurr, AK,1992 Regions WEB

USGS

Deborah L. McGuinness


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Tropopause Regions WEB

http://aerosols.larc.nasa.gov/volcano2.swf


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Atmosphere Use Case Regions WEB

  • Determine the statistical signatures of both volcanic and solarforcings on the height of the tropopause

    From paleoclimate researcher – Caspar Ammann – Climate and Global Dynamics Division of NCAR - CGD/NCAR

    Layperson perspective:

    - look for indicators of acid rain in the part of the atmosphere we experience…

    (look at measurements of sulfur dioxide in relation to sulfuric acid after volcanic eruptions at the boundary of the troposphere and the stratosphere)

    Nasa funded effort with Fox - NCAR, Sinha - Va. Tech, Raskin - JPL

Deborah L. McGuinness


Use case detail a volcano erupts l.jpg
Use Case detail: Regions WEBA volcano erupts

  • Preferentially it’s a tropical mountain (+/- 30 degrees of the equator) with ‘acidic’ magma; more SiO2, and it erupts with great intensity so that material and large amounts of gas are injected into the stratosphere.

  • The SO2gasconverts to H2SO4 (Sulfuric Acid) + H2O (75% H2SO4 + 25% H2O). The half life of SO2 is about 30 - 40 days.

  • The sulfuric acidcondensates to little super-cooledliquiddroplets. These are the volcanic aerosol that will linger around for a year or two.

  • Brewer Dobson Circulation of the stratosphere will transportaerosol to higher latitudes. The particles generate great sunsets, most commonly first seen in fall of the respective hemisphere. The sunlight gets partially reflected, some part gets scattered in the forward direction.

  • Result is that the direct solar beam is reduced, yet diffuse skylight increases. The scattering is responsible for the colorful sunsets as more and more of the blue wavelength are scattered away.in mid-latitudes the volcanic aerosol starts to settle, but most efficient removal from the stratosphere is through tropopause folds in the vicinity of the stormtracks.

  • If particles get over the pole, which happens in spring of the respective hemisphere, then they will settle down and fall onto polar icecaps. Its from these icecaps that we recover annual records of sulfateflux or deposit.

  • We get icecores that show continuous deposition information. Nowadays we measure sulfate or SO4(2-). Earlier measurements were indirect, putting an electric current through the ice and measuring the delay. With acids present, the electric flow would be faster.

  • What we are looking for are pulse likeevents with a build up over a few months (mostly in summer, when the vortex is gone), and then a decay of the peak of about 1/e in 12 months.

  • The distribution of these pulses was found to follow an extreme value distribution (Frechet) with a heavy tail.

Deborah L. McGuinness


Use case detail climate l.jpg
Use Case detail: Regions WEB… climate

  • So reflection reduces the total amount of energy, forward scattering just changes the beam, path length, but that's it.

  • The dryfogs in the sky (even after thunderstorm) still up there, thus stratosphere not troposphere.

  • The tropical reservoir will keep delivering aerosol for about two years after the eruption.

  • The particles are excellent scatterers in short wavelength. They do absorb in NIR and in IR. Because of absorption, there is a local temperature change in the lower stratosphere.

  • This temperature change will cause some convective motion to further spread the aerosol, and second: Its good factual stuff. Once it warms up, it will generate a temperature gradient. Horizontal temperaturegradients increase the baroclinicity and thus storms, and they speedup the local zonalwinds. This change in zonal wind in high latitudes is particularly large in winter. This increased zonal wind (Westerly) will remove all coldair that tries to buildup over winter in high arctic.

  • Therefore, the temperature anomaly in winter time is actually quite okay.

  • Impact of volcanoes is to cool the surface through scattering of radiation.

  • In winter time over the continents there might be some warming. In the stratosphere, the aerosol warm.

  • The amount of GHG emitted is comparably small to the reservoir in the air.

  • The hydrologic cycle responds to a volcanic eruption.

Deborah L. McGuinness


Atmosphere portions from sweet l.jpg
Atmosphere (portions from SWEET) Regions WEB

Deborah L. McGuinness


Atmosphere ii l.jpg
Atmosphere II Regions WEB

Deborah L. McGuinness



A few observations worth noting l.jpg
A few observations worth noting Regions WEB

  • CMAPS have been convenient knowledge capture tools

  • We facilitate knowledge acquisition meetings AND provide a starting point

  • We are experiencing good reuse of ontologies and infrastructure

  • Next – Quick VSTO walk thru

Deborah L. McGuinness


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www.vsto.org Regions WEB

Deborah L. McGuinness



Slide23 l.jpg

Semantic filtering by domain or instrument hierarchy Regions WEB

Partial exposure of Instrument class hierarchy - users seem to like this

Deborah L. McGuinness



Slide25 l.jpg

Inferred plot type and return required axes data Regions WEB

Deborah L. McGuinness


Slide26 l.jpg
VSTO Regions WEB

  • Conceptual model and architecture developed by combined team; KR experts, domain experts, and software engineers

  • Semantic framework developed and built with a small, cohesive, carefully chosen team in a relatively short time (deployments in 1st year)

  • Production portal released, includes security, etc. with community migration (and so far endorsement)

  • VSTO ontology version 1.0, (vsto.owl)

  • Web Services encapsulation of semantic interfaces

  • More Solar Terrestrial use-cases to drive the completion of the ontologies - filling out the instrument ontology

  • Using ontologies in other applications (volcanoes, climate, …)

Deborah L. McGuinness


Semantic web methodology and technology development process l.jpg
Semantic Web Methodology and Technology Development Process Regions WEB

  • Establish and improve a well-defined methodology vision for Semantic Technology-based application development

Adopt Technology Approach

Leverage Technology Infrastructure

Expert Review & Iteration

Rapid Prototype

Open World: Evolve, Iterate, Redesign, Redeploy

Use Tools

Analysis

Use Case

Develop model/ ontology

Small Team, mixed skills

Joint with P. Fox

Deborah L. McGuinness


Benefits l.jpg
Benefits Regions WEB

  • Unified query workflow

  • Decreased input requirements for query: in one base reducing the number of selections from eight to three

  • Interface generates only syntactically correct queries: which was not always true in previous implementations without semantics

  • Semantic query support: by using background ontologies and a reasoner, our application has the opportunity to only expose coherent queries

  • Semantic integration: in the past users had to remember (and maintain codes) to account for numerous different ways to combine and plot the data whereas now semantic mediation provides the level of sensible data integration required

    • understanding of coordinate systems, relationships, data synthesis, transformations, etc.

  • A broader range of potential users (PhD scientists, students, professional research associates and those from outside the fields)

Deborah L. McGuinness


Explanation transition l.jpg
Explanation Transition Regions WEB

Deborah L. McGuinness


General motivation l.jpg
General Motivation Regions WEB

Provide interoperableknowledgeprovenance infrastructure that supports explanations of sources, assumptions, learned information, and answers as an enabler for trust.

Interoperability– as systems use varied sources and multiple information manipulation engines, they benefit more from encodings that are shareable & interoperable

Provenance– if users (humans and agents) are to use and integrate data from unknown, unreliable, or evolving sources, they need provenance metadata for evaluation

Explanation/Justification– if information has been manipulated (i.e., by sound deduction or by heuristic processes), information manipulation trace information should be available

Trust – if some sources are more trustworthy than others, representations should be available to encode, propagate, combine, and (appropriately) display trust values

Deborah L. McGuinness


Requirements gathered from l.jpg
Requirements gathered from… Regions WEB

DARPA Agent Markup Language (DAML)

Enable the next generation of the web

DARPA Personal Assistant that Learns (PAL)

Enable computer systems that can reason, learn, be told what to do, explain actions, reflect on their experience, & respond robustly to surprise

DARPA Integrated Learning (IL)

Enable learning general plans or processes from human users by being shown one example by opportunistically assembling knowledge from many different sources, including generating it by reasoning, in order to learn.

DARPA Rapid Knowledge Formation (RKF)

Allow distributed teams of subject matter experts to quickly and easily build, maintain, and use knowledge bases without need for specialized training

DTO Novel Intelligence for Massive Data (NIMD)

Avoid strategic surprise by helping analysts be more effective (focus attention on critical information and help analyze/prune/refine/explain/reuse/…)

DTO IKRIS – Interoperable knowledge representation for intelligence apps

NSF & NASA Scientific Data Integration (NSF Virtual Observatories (VSTO), NSF GEON, NASA SESDI, NASA SKIF, …)

NSF Cybertrust Transparent Accountable Data Mining (TAMI)

Govt Classified applications that must defend their conclusions

Deborah L. McGuinness


Inference web infrastructure primary collaborators pinheiro da silva ding chang fikes glass zeng l.jpg

Files/WWW Regions WEB

Toolkit

Trust computation

IWTrust

Semantic Discovery Service

(DAML/SNRC)

OWL-S/BPEL

Proof Markup Language (PML)

End-user friendly

visualization

IW Explainer/

Abstractor

CWM

(NSF TAMI)

N3

Expert friendly

Visualization

Trust

KIF

JTP

(DAML/NIMD)

IWBrowser

search engine

based publishing

Justification

SPARK

(DARPA CALO)

SPARK-L

IWSearch

Provenance

provenance

registration

UIMA

(DTO NIMD

Exp Aggregation)

Text Analytics

IWBase

Inference Web Infrastructure primary collaborators Pinheiro da Silva, Ding, Chang, Fikes, Glass, Zeng

  • Framework for explaining question answering tasks by

  • abstracting, storing, exchanging,

  • combining, annotating, filtering, segmenting,

  • comparing, and rendering proofs and proof fragments

  • provided by question answerers.

Deborah L. McGuinness


How pml works l.jpg
How PML Works Regions WEB

isQueryFor

IWBase

Query foo:query1

<formal internal structured query>

Question foo:question1

<Input language question>

hasAnswer

Justification Trace

hasLanguage

NodeSet foo:ns1

(hasConclusion …)

Language

hasInferencEngine

fromQuery

isConsequentOf

InferenceEngine

InferenceStep

hasRule

InferenceRule

hasAntecendent

Source

NodeSet foo:ns2

(hasConclusion …)

hasVariableMapping

Mapping

isConsequentOf

fromAnswer

hasSourceUsage

hasSource

SourceUsage

InferenceStep

usageTime …

Deborah L. McGuinness


Slide34 l.jpg

PML in Swoop Regions WEB

Deborah L. McGuinness


Example usage l.jpg
Example Usage Regions WEB

  • DARPA’s PAL program – explaining cognitive assistant suggestions.

  • Video

Deborah L. McGuinness


Explainer strategy for cognitive assistants l.jpg
Explainer Strategy (for cognitive assistants) Regions WEB

Present

  • Query

  • Answer

  • Abstraction of justification (using PML encodings)

  • Provide access to meta information

  • Suggests drill down options (also provides feedback options)

Deborah L. McGuinness


Architecture for explaining task processing l.jpg

Task State Database Regions WEB

Architecture for Explaining Task Processing

TaskLearner1

Task Manager (TM)

TaskLearner2

TaskLearner3

Collaboration Agent

Explanation Dispatcher

TM Explainer

TM Wrapper

Justification Generator

Deborah L. McGuinness


Task explanation l.jpg
Task Explanation Regions WEB

Ability to ask “why” at any point…

Context appropriate follow-up questions are presented

Deborah L. McGuinness


Iwbrowser browse debug tami l.jpg
IWBrowser - Browse & Debug (TAMI) Regions WEB

Deborah L. McGuinness


Multiple interfaces browsing proofs l.jpg
Multiple Interfaces… Browsing Proofs Regions WEB

Deborah L. McGuinness


Browsing debugging l.jpg
Browsing & Debugging Regions WEB

Deborah L. McGuinness


Example abstraction using tactics l.jpg
Example Abstraction (using tactics) Regions WEB

Deborah L. McGuinness


Slide43 l.jpg
Intelligence Tool Explanation Regions WEB(similar to other applications that reason with statements that may not be 100% correct)

Deborah L. McGuinness


Follow up metadata l.jpg
Follow-up : Metadata Regions WEB

Deborah L. McGuinness


Follow up assumptions l.jpg
Follow-up: Assumptions Regions WEB

Deborah L. McGuinness


Explaining extracted entities techies l.jpg
Explaining Extracted Entities Regions WEB(Techies)

Sentences in English

Sentences in

annotated English

Sentences in

logical format,

i.e., KIF

Deborah L. McGuinness


Trustworthiness of extracted entities l.jpg
Trustworthiness of Extracted Entities Regions WEB

A trustworthy conclusion from IBM STAG KDD-model Annotator

A highlytrustworthy conclusion from IBM EAnnotator

The combined conclusion is highly trustworthy

Deborah L. McGuinness


Estimated trustworthiness of the ibm extraction and integration components l.jpg
Estimated trustworthiness of the IBM extraction and integration components

IBM Cross-Annotator Coreference Resolver 0.82 IBM Cross-Document Coreference Resolver 0.63 IBM EAnnotator 0.91 IBM GlossOnt 0.33 IBM JResporator 0.31 IBM KANI holdsDuring Relation Detector 0.20 IBM Knowledge Integrator 0.88 IBM Knowledge Structures Group's Relation Detector 0.94 IBM Statistical Text Analytics Group's ACE-model Annotator 0.80 IBM Statistical Text Analytics Group's KDD-model Annotator 0.73 IBM TAF/Talent plus a collection of miscellaneous TFST grammars 0.78 IBM Talent time annotator 0.83

Deborah L. McGuinness


Trustworthiness of ramazi report l.jpg
Trustworthiness of Ramazi report integration components

From FBI

From intercepts

From CIA

Deborah L. McGuinness


Trustworthiness of revised ramazi report l.jpg
Trustworthiness of revised Ramazi report integration components

This text fragment changed from “Neutral” to “Trustworthy” after it was revised by an analyst (the phone number was corrected).

Deborah L. McGuinness


Trust representation calculation propagation l.jpg
Trust Representation, Calculation, & Propagation integration components

  • - Begin with simple representation of trust

  • Present trust coloring view

  • Calculation & propagation research options


A sample pml encoding http inferenceweb stanford edu 2006 02 example1 iw wiki owl l.jpg
A Sample PML encoding integration componentshttp://inferenceweb.stanford.edu/2006/02/example1-iw-wiki.owl

fragment

<iw:NodeSet rdf:about="http://foto.stanford.edu/mediawiki-1.4.12/index.php/Natural_number">

<In mathematics, a natural number is either a positive integer … </iw:hasConclusion>

<iw:hasLanguage rdf:resource="http://inferenceweb.stanford.edu/registry/LG/English.owl#English"/>

<iw:isConsequentOf>

<iw:InferenceStep>

<iw:hasRule rdf:resource="http://inferenceweb.stanford.edu/registry/DPR/Told.owl#Told"/>

<iw:hasInferenceEngine rdf:resource="http://inferenceweb.stanford.edu/registry/IE/CitationTrust.owl#CitationTrust"/>

<iw:hasSourceUsage>

<iw:SourceUsage>

<iw:hasSource>

<iw:Source rdf:about="http://inferenceweb.stanford.edu/wp/registry/PER/Alexandrov.owl#Alexandrov"/>

</iw:hasSource>

</iw:SourceUsage>

</iw:hasSourceUsage>

</iw:InferenceStep>

</iw:isConsequentOf>

</iw:NodeSet>

<iw:AggregatedTrustRelation>

<iw:hasTrustingParty rdf:resource="http://inferenceweb.stanford.edu/wp/registry/ORG/wikipedia.owl#wikipedia"/>

<iw:hasTrustedParty rdf:resource="http://foto.stanford.edu/mediawiki-1.4.12/index.php/Natural_number"/>

<iw:hasTrustValue rdf:datatype="http://www.w3.org/2001/XMLSchema#float">0.1766</iw:hasTrustValue>

</iw:AggregatedTrustRelation>

<iw:AggregatedTrustRelation>

<iw:hasTrustingParty rdf:resource="http://inferenceweb.stanford.edu/wp/registry/ORG/wikipedia.owl#wikipedia"/>

<iw:hasTrustedParty rdf:resource="http://inferenceweb.stanford.edu/wp/registry/PER/Alexandrov.owl#Alexandrov"/>

<iw:hasTrustValue rdf:datatype="http://www.w3.org/2001/XMLSchema#float">0.1766</iw:hasTrustValue>

</iw:AggregatedTrustRelation>

fragment trust

author trust


Conclusion l.jpg
Conclusion integration components

Semantic Web languages and tools are evolving and currently are enabling next generation collaboration and applications

Some examples here include support for

- explainable question answering systems

- semantic integration of information

- trustable applications

- usable, integrated, explainable virtual observatories

For more info on talk topics:

- Inference Web - iw.stanford.edu (OWL - www.w3.org/TR/owl-features/ )

  • Virtual Solar Terrestrial Observatory- www.vsto.org

  • Semantic Technology Conference - www.semantic-conference.com/

  • AAAI workshops: Explanation-Aware Computing, Semantic eScience, IAAI

  • Special Issue for Elsevier Computers and GeoSciences, New Springer Journal – Journal of Earth Science Informatics, …

    - [email protected]

Deborah L. McGuinness


More information l.jpg
More Information integration components

  • http://iw.stanford.edu/2.0/publications.html

  • Best Inference Web Paper: Deborah L. McGuinness and Paulo Pinheiro da Silva. Explaining Answers from the Semantic Web: The Inference Web Approach. Journal of Web Semantics. Vol.1 No.4., pages 397-413, October 2004.

  • -Best PML paper: Paulo Pinheiro da Silva, Deborah L. McGuinness and Richard Fikes. A Proof Markup Language for Semantic Web Services. Information Systems. Volume 31, Issues 4-5, June-July 2006, Pages 381-395.

  • Best Requirements paper (from interviewing Intelligence Analysts)

  • Cowell, McGuinness, Varley, &Thurman. Knowledge-Worker Requirements for Next Generation Query Answering and Explanation Systems. Workshop on Intelligent User Interfaces for Intelligence Analysis, (IUI 2006), Sydney, Australia

  • Best UI paper - Explanation Interfaces for the Semantic Web. SWUI ’06. http://www.ksl.stanford.edu/KSL_Abstracts/KSL-06-14.html

  • VSTO. McGuinness, Fox, Cinquini, Darnell, West, Benedict, Garcia, Middleton. Ontology-Enabled Virtual Observatories: Semantic Integration in Practice. ISWC06. Athens, Georgia, USA. ksl.stanford.edu/KSL_Abstracts/KSL-06-20.html www.vsto.org

Deborah L. McGuinness


Extras l.jpg
Extras integration components

Deborah L. McGuinness


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KSL Wine Agent integration componentsSemantic Web Integration

  • Wine Agent receives a meal description and retrieves a selection of matching wines available on the Web, using an ensemble of emerging standards and tools:

    • OWL for representing a domain ontology of foods, wines, their properties, and relationships between them

    • JTP theorem prover for deriving appropriate pairings

    • OWL-QL for querying a knowledge base consisting of the above

    • Inference Web for explaining and validating the response

    • [Web Services for interfacing with vendors]

    • Utilities for conducting and caching the above transactions

Deborah L. McGuinness


Slide57 l.jpg

Deborah L. McGuinness integration components


Processing l.jpg
Processing integration components

  • Given a description of a meal,

    • Use OWL-QL to state a premise (the meal) and query the knowledge base for a suggestion for a wine description or set of instances

    • Use JTP to deduce answers (and proofs)

    • Use Inference Web to explain results (descriptions, instances, provenance, reasoning engines, etc.)

    • Access relevant web sites (wine.com, …) to access current information

    • Use OWL-S for markup and protocol*

      This general scheme used in other projects like TAMI, IL, etc.

      http://www.ksl.stanford.edu/projects/wine/explanation.html

Deborah L. McGuinness


Ontology spectrum l.jpg
Ontology Spectrum integration components

Thesauri

“narrower

term”

relation

Frames

(properties)

Disjointness,

Inverse,

part-of…

Formal

is-a

Catalog/

ID

Informal

is-a

Formal

instance

General

Logical

constraints

Terms/

glossary

Value Restrs.

Markup such as DAML+OIL, OWL can be used to encode the spectrum

Originally from AAAI 1999- Ontologies Panel – updated by McGuinness

Deborah L. McGuinness


Issues for virtual observatories l.jpg
Issues for Virtual Observatories integration components

  • Scaling to large numbers of data providers

  • Crossing disciplines

  • Security, access to resources, policies

  • Branding and attribution (where did this data come from and who gets the credit, is it the correct version, is this an authoritative source?)

  • Provenance/derivation (propagating key information as it passes through a variety of services, copies of processing algorithms, …)

  • Data quality, preservation, stewardship, rescue

  • Interoperability at a variety of levels (~3)

Semantics can help with many of these

Deborah L. McGuinness


Impact virtual observatories changing science l.jpg
Impact: Virtual Observatories Changing Science integration components

Scientists: What if you…

  • could not only use your data and tools but remote colleague’s data and tools?

  • understood their assumptions, constraints, etc and could evaluate applicability?

  • knew whose research currently (or in the future) would benefit from your results?

  • knew whose results were consistent (or inconsistent) with yours?…

    Funders/Managers: What if you …

  • could identify how one research effort would support other efforts?

  • (and your fundees/employees) could reuse previous results?

  • (and your fundees/employees) could really interoperate?

    CS: What if you…

  • could apply your techniques across very large distributed teams of people with related but different apps?

  • could compare your techniques with colleagues trying to solve similar problems?

Deborah L. McGuinness


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